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Microsoft, Google Beat Humans at Image Recognition

Deep learning algorithms compete at ImageNet challenge
2/18/2015 08:15 AM EST
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R_Colin_Johnson
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Re: Image recognition, pattern matching, etc.
R_Colin_Johnson   2/18/2015 2:08:21 PM
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Terry.Bollinger: You make some very good points--its almost as if computers are not a good model on which to build electronic brain's. Brain's are sloppy--and individual can't remember long-numbers easily, for instance--companies like MicroBial Robotics, for instnace, are basing their brain-like aspirations not on silicon, but on real biological materials. Maybe we should stop trying to make computers biological-like, and just exploit them for what they do best? Then let the biologista use synthetic life principles to build the robotic brains of the future.

Terry.Bollinger
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Re: Image recognition, pattern matching, etc.
Terry.Bollinger   2/18/2015 1:44:43 PM
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Both long-term massive project and short-term challenges play valuable roles in research, but for developing a deep theory of cognition with accompanying devices to implement that theory, I'd place my money on the short-term challenge approach. If those challenges are also available to individuals working within big projects or big research organizations, so much the better, since that situation allows creative people to make good use of the deep resources of such organizations.

Short-term challenges are important because the deepest insights into hard problems usually arise from individuals following their own curiosity, often with flagrant disregard for the status quo. Challenges that allows such individuals to focus on hard problems give them a chance to follow their different drummers. And sometimes it works! Every so often, they scout out some unique path no one knew existed, and so end up giving everyone access to an entirely new and unexpected world of opportunties.

Both John Bell and his quantum inequality and Kary Mullis with polymerase chain reaction are good examples of this different-drummer effect.

Why do large organization have trouble getting similarly creative? Once reason is collective bias, that is, the tendency for everyone in a large group or community to reward each other, often subtly and unintentionally, for following the same traditional drummer.

For example, as Junko noted, understanding human cognition and intellitgence currently suffers from a sort of aren't-computers-great bias that unconsciously encourages most of us to default to sequential problem solution methods, rather than parallel ones.

But our biases go far deeper than that: We are also precision and perfection biased, expecting every computation to give an exact number and the same result every time. Alas, from DNA up through human intelligence, biological systems just don't work that way. Bacteria for example provably use sloppiness and diversity as a valuable component of their calculation processes, in ways that we are just barely starting to understand. This same intentional use of unexpected diversity can also be seen in many other biological processes, notably including neural voting processes.

Our overall understanding of such biological examples is expanding rapidly these days, and with that expansion I fully expect to see some of those folks who listen to different drummers come up with startling new insights into the fundamental nature of intelligence and cognition. I think the next few years will be very interesting indeed.

 

R_Colin_Johnson
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Re: Image recognition, pattern matching, etc.
R_Colin_Johnson   2/18/2015 12:02:39 PM
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Everybody doing neural networks today using Deep Learning, including IBM and Microsoft are using the parallel architectures that fit the problem so well. IBM is building its own specialized parallel machines for Deep Learning with neural networks and has designed its own Corelets language to program it. IBM's mimics many aspects of the way the human brain works--like the digital spiking outputs--but most of the contestants in the ImageNet Challenge, including Microsoft, are just simulating the synatptic weights between neurons with floating-point numbers on standard parallel architectures, and getting good results. (You'll never build and electronic brain that way though.) Several others are using DARPA money to emulate the brain in great detail and the E.U. has the most ambitious undertaking called The Human Brain Project. What do you think is more important, short-term goals like the ImageNet Challenge, or long-term goals like an electronic brains for robots?

junko.yoshida
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Image recognition, pattern matching, etc.
junko.yoshida   2/18/2015 9:33:12 AM
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Things like image recognition, deep learnig are clearly one of the hottest areas many tech companies are working on.

We just posted a story about NeuroMem, a startup which is using the massively parallel computing for pattern matching. (http://www.eetimes.com/document.asp?doc_id=1325690)

Has anyone at IBM, or Microsoft, talked about the limitations of sequential computing architecture when running algorithms that are essentially so parallel in nature?

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